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  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'utils'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-2-50ff88f66368>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0maltair\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0malt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mattributionpriors\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0meager_ops\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mpath_explain\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mPathExplainerTF\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mshap\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/expected_hessians/path_explain/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mexplainers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath_explainer_tf\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mPathExplainerTF\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mutils\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mset_up_environment\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msoftplus_activation\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      9\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscatter\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mscatter_plot\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msummary\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0msummary_plot\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'utils'"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import altair as alt\n",
    "from attributionpriors import eager_ops\n",
    "from path_explain import PathExplainerTF\n",
    "import shap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "PathExplainerTF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "(x_train, y_train), (x_test, y_test) = tf.keras.datasets.boston_housing.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "column_names = ['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD',\n",
    "                'TAX', 'PTRATIO', 'B', 'LSTAT']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
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   "outputs": [],
   "source": [
    "mean = x_train.mean(axis = 0)\n",
    "std  = x_train.std(axis  = 0)\n",
    "x_train = (x_train - mean) / std\n",
    "x_test  = (x_test  - mean) / std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense (Dense)                (None, 64)                896       \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 64)                4160      \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 1)                 65        \n",
      "=================================================================\n",
      "Total params: 5,121\n",
      "Trainable params: 5,121\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "def build_model():\n",
    "    model = tf.keras.Sequential([\n",
    "        tf.keras.layers.Dense(64, activation=tf.keras.activations.relu,\n",
    "                           input_shape=(x_train.shape[1],)),\n",
    "        tf.keras.layers.Dense(64, activation=tf.keras.activations.relu),\n",
    "        tf.keras.layers.Dense(1)\n",
    "    ])\n",
    "\n",
    "    optimizer = tf.keras.optimizers.SGD(0.0001)\n",
    "\n",
    "    model.compile(loss='mse',\n",
    "                optimizer=optimizer,\n",
    "                metrics=['mae'])\n",
    "    return model\n",
    "\n",
    "model = build_model()\n",
    "model.summary()"
   ]
  },
  {
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   "outputs": [],
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    "history = model.fit(x_train, y_train, epochs=500,\n",
    "                    validation_split=0.2, verbose=0)"
   ]
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",
      "text/plain": [
       "<VegaLite 3 object>\n",
       "\n",
       "If you see this message, it means the renderer has not been properly enabled\n",
       "for the frontend that you are using. For more information, see\n",
       "https://altair-viz.github.io/user_guide/troubleshooting.html\n"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_df = pd.DataFrame(history.history)\n",
    "data_df['epoch'] = history.epoch\n",
    "melt_df = pd.melt(data_df, id_vars=['epoch'], var_name='split', value_name='loss')\n",
    "alt.Chart(melt_df).mark_line().transform_filter(\n",
    "    'datum.epoch > 20'\n",
    ").encode(\n",
    "    alt.X('epoch:Q'),\n",
    "    alt.Y('loss:Q'),\n",
    "    alt.Color('split:N')\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set mean absolute error: $3338.50\n"
     ]
    }
   ],
   "source": [
    "[loss, mae] = model.evaluate(x_test, y_test, verbose=0)\n",
    "\n",
    "print(\"Test set mean absolute error: ${:7.2f}\".format(mae * 1000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 102/102 [00:08<00:00, 11.93it/s]\n"
     ]
    }
   ],
   "source": [
    "explainer = PathExplainerTF(model)\n",
    "eg_from_path = explainer.attributions(x_test,\n",
    "                                      x_train,\n",
    "                                      batch_size=50,\n",
    "                                      num_samples=1000,\n",
    "                                      use_expectation=True,\n",
    "                                      verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "grad_explainer = shap.GradientExplainer(model, x_train, batch_size=50)\n",
    "eg_from_shap = grad_explainer.shap_values(x_test, nsamples=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
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",
      "text/plain": [
       "<VegaLite 3 object>\n",
       "\n",
       "If you see this message, it means the renderer has not been properly enabled\n",
       "for the frontend that you are using. For more information, see\n",
       "https://altair-viz.github.io/user_guide/troubleshooting.html\n"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "completeness_df = pd.DataFrame({\n",
    "    'Output Difference': model(x_test).numpy()[:, 0] - np.mean(model(x_train)),\n",
    "    'Path Attributions': np.sum(eg_from_path, axis=-1)[0]\n",
    "})\n",
    "alt.Chart(completeness_df).mark_point(filled=True).encode(\n",
    "    alt.X('Output Difference:Q'),\n",
    "    alt.Y('Path Attributions:Q')\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.vegalite.v3+json": {
       "$schema": "https://vega.github.io/schema/vega-lite/v3.4.0.json",
       "config": {
        "mark": {
         "tooltip": null
        },
        "view": {
         "height": 300,
         "width": 400
        }
       },
       "data": {
        "name": "data-648c86ea92f0530ccb01410edfc3a784"
       },
       "datasets": {
        "data-648c86ea92f0530ccb01410edfc3a784": [
         {
          "Path Attributions": -4.8253234592871665,
          "Shap Attributions": -4.8472700119018555
         },
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",
      "text/plain": [
       "<VegaLite 3 object>\n",
       "\n",
       "If you see this message, it means the renderer has not been properly enabled\n",
       "for the frontend that you are using. For more information, see\n",
       "https://altair-viz.github.io/user_guide/troubleshooting.html\n"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "completeness_df = pd.DataFrame({\n",
    "    'Shap Attributions': eg_from_shap[0].flatten(),\n",
    "    'Path Attributions': eg_from_path.flatten()\n",
    "})\n",
    "alt.Chart(completeness_df).mark_point(filled=True).encode(\n",
    "    alt.X('Shap Attributions:Q'),\n",
    "    alt.Y('Path Attributions:Q')\n",
    ")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
